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Related Experiment Video

Updated: Nov 4, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

142

Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network.

Quan Zhou1, Jianhua Shan1, Wenlong Ding1

  • 1Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma'anshan, China.

Frontiers in Robotics and AI
|May 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cough recognition method using Mel-spectrograms and a Convolutional Neural Network (CRN). The CRN effectively distinguishes cough sounds for potential COVID-19 patient screening.

Keywords:
CNNCOVID-19audiocough recognitiondeep learningmel-spectrogram

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Area of Science:

  • * Acoustic Signal Processing
  • * Machine Learning for Health Monitoring

Background:

  • * Accurate detection of specific sounds is crucial in various real-world scenarios.
  • * The COVID-19 pandemic highlighted the need for rapid identification of potential infectious disease indicators, such as coughing.

Purpose of the Study:

  • * To develop and evaluate an automated method for recognizing cough sounds.
  • * To differentiate coughs from other environmental sounds for public health applications.

Main Methods:

  • * Utilized Mel-spectrograms to represent audio features.
  • * Employed a Convolutional Neural Network (CNN) architecture, termed the Cough Recognition Network (CRN).

Main Results:

  • * The proposed Cough Recognition Network (CRN) demonstrated effective distinction of cough sounds.
  • * The method shows promise for identifying coughs in complex acoustic environments.

Conclusions:

  • * The Mel-spectrogram and CRN-based approach provides an effective solution for cough recognition.
  • * This technology can aid in preliminary screening and monitoring of respiratory illnesses.